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Related Concept Videos

RNA-seq03:21

RNA-seq

10.6K
RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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The technique...
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Related Experiment Video

Updated: Oct 18, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

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Advances in spatial transcriptomic data analysis.

Ruben Dries1,2,3, Jiaji Chen1, Natalie Del Rossi4

  • 1Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA.

Genome Research
|October 2, 2021
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics reveals tissue organization at high resolution. Computational methods and analysis pipelines are crucial for interpreting this data, advancing biological understanding in health and disease.

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial transcriptomics offers unprecedented resolution for studying tissue organization.
  • It provides insights into biological processes in health and disease beyond traditional methods.
  • Computational tools are essential for extracting meaningful biological signals from complex spatial transcriptomic data.

Purpose of the Study:

  • To review the current state of spatial transcriptomic data analysis methods.
  • To discuss computational approaches for overcoming technology-specific limitations.
  • To explore how analysis tools and pipelines facilitate the study of spatial organization and cell-cell communication.

Main Methods:

  • Review of existing computational methods for spatial transcriptomic data analysis.
  • Discussion of algorithms for quantifying spatial organization and cell-cell communication.
  • Overview of integrative pipelines that streamline end-to-end data analysis.

Main Results:

  • Various computational approaches address limitations like spatial resolution, gene coverage, sensitivity, and technical biases.
  • Downstream analysis tools enable the quantification of spatial organization and cell-cell interactions.
  • Integrative pipelines simplify complex data analysis for biologists.

Conclusions:

  • Computational methods are vital for unlocking the potential of spatial transcriptomics.
  • Advanced analysis tools and pipelines are key to understanding tissue architecture and biological mechanisms.
  • This review provides a comprehensive overview of the current landscape of spatial transcriptomic data analysis.